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Clustering social networks using interaction semantics and sentics

  • Praphul Chandra*
  • , Erik Cambria
  • , Amir Hussain
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, however, online social groups tend to form for extremely varied and multi-faceted reasons. This makes very difficult to group members of the same social network in subsets in a way that certain types of contents are shared with just certain types of friends. Moreover, such a task is usually too tedious to be performed manually and too complex to be performed automatically. In this work, we propose a new approach for automatically clustering social networks, which exploits interaction semantics and sentics, that is, the conceptual and affective information associated with the interactive behavior of online social network members.

Original languageEnglish
Title of host publicationAdvances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
Pages379-385
Number of pages7
EditionPART 1
DOIs
StatePublished - 2012
Externally publishedYes
Event9th International Symposium on Neural Networks, ISNN 2012 - Shenyang, China
Duration: 11 Jul 201214 Jul 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Symposium on Neural Networks, ISNN 2012
Country/TerritoryChina
CityShenyang
Period11/07/1214/07/12

Keywords

  • NLP
  • Sentic Computing
  • Social Network Analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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